feat: add multimodal search and granular performance diagnostics

docs/BUILDING.md

@@ -22,6 +22,7 @@ The Python CMake package is needed only when CMake is absent from PATH.
 python -m pip install cmake
 python tools/embeddinggemma2/build_runtime.py --backend cpu --jobs 2
 python tools/embeddinggemma2/stage_runtime.py --backend cpu
+python tools/media-runtime/provision.py --stage
 python tools/embeddinggemma2/download.py
 $env:SEARCHI_FORCE_CPU = '1'
 $env:SEARCHI_GEMMA_MODEL_DIR = (Resolve-Path .searchi/models/embeddinggemma2).Path
@@ -38,8 +39,7 @@ The worker is statically linked with the MSVC CRT on Windows. Staging copies
 it and the llama.cpp license into the desktop resources directory. Add
 `--bundle-model` to staging for an offline model bundle after downloading;
 otherwise desktop setup provisions verified models on first launch. Search
-itself performs no network inference or downloads. Media weights are staged
-for future integration; the current worker supports text only.
+itself performs no network inference or downloads. The worker now uses the verified media projector for genuine image/audio embeddings. The FFmpeg script stages verified standalone decoders and their notices.
 
 The desktop is a standalone Cargo workspace and needs separate validation:
 
@@ -50,7 +50,7 @@ npm ci
 npm run format:check
 npm test
 npm run build
-$env:WEBVIEW2_ADDITIONAL_BROWSER_ARGUMENTS = '--disable-gpu'
+$env:WEBVIEW2_ADDITIONAL_BROWSER_ARGUMENTS = '--disable-gpu --disable-accelerated-video-decode'
 npm run tauri -- dev
 ```
 
@@ -67,13 +67,18 @@ Linux release. On a native Linux build host, run from the repository root:
 ```sh
 python3 tools/embeddinggemma2/build_runtime.py --backend cpu --jobs 2
 python3 tools/embeddinggemma2/download.py
+python3 tools/media-runtime/provision.py
 export SEARCHI_FORCE_CPU=1
 cargo build --release -p searchi-cli --locked
 ./target/release/searchi index --db searchi.sqlite --root /path/to/documents
-./target/release/searchi embed --db searchi.sqlite --model-dir .searchi/models/embeddinggemma2 --embedding-worker .searchi/runtime/cpu/searchi-gemma-worker --cpu-threads 1
+./target/release/searchi embed --db searchi.sqlite --model-dir .searchi/models/embeddinggemma2 --embedding-worker .searchi/runtime/cpu/searchi-gemma-worker --ffmpeg .searchi/media-runtime/bin/ffmpeg --ffprobe .searchi/media-runtime/bin/ffprobe --cpu-threads 1
 ./target/release/searchi search --db searchi.sqlite 'supplier cancellation terms' --model-dir .searchi/models/embeddinggemma2 --embedding-worker .searchi/runtime/cpu/searchi-gemma-worker --cpu-threads 1
 ```
 
+`embed` builds document, image, video and audio/music vectors by default. Use
+`--text-only` explicitly to omit media. Default media indexing needs paired
+FFmpeg/FFprobe paths or packaged helpers beside the installed CLI.
+
 `searchi setup --model-dir PATH` is an alternative verified model-provisioning
 command. It provisions model assets, not the native worker. Package the worker
 separately and pass its path to headless embedding/search. Each short-lived CLI
@@ -114,5 +119,9 @@ installers, stage the target worker, retain dependency/model notices, verify
 clean-machine launch and setup, run native Google smoke tests, and complete
 upgrade/migration, signatures, and rollback checks. Optional offline bundles
 need both verified model files. See [semantic bounds and evidence](SEMANTIC.md)
-for current text-only, corpus, and performance limitations. Keep signing keys,
-repository tokens, and model credentials outside source control.
+for current corpus, localization, and performance limitations. Keep signing keys,
+repository tokens, and model credentials outside source control.
+FFmpeg provisioning supports manifest-pinned Windows/Linux x86-64 and ARM64 artifacts;
+only Windows x86-64 has executed here. macOS decoder packaging remains a gate.
+See [media runtime provenance](../tools/media-runtime/README.md) and
+[measured media limitations](MEDIA-QUALITY.md).